What This Category of Data Looks Like
Data for large state-owned bank intelligent due diligence reports comes from four primary sources: internal credit management systems, People's Bank of China credit reporting submission interfaces, quarterly financial report disclosure documents, and regulatory compliance reports. Data update cycles are managed in layers: credit ledger data updates daily, financial report data updates quarterly, and regulatory reports update monthly. Document structure splits into two parts: structured fields and unstructured text. Structured fields cover credit limits, overdue days, regulatory ratings, and similar items. Their units are ten thousand yuan, days, and letter-based rating codes, respectively. The unstructured section includes long text content such as credit approval opinions and risk assessment reports.
What Constraints These Characteristics Impose on Tool Calling and Plugins
The large number of structured fields and fixed units require tools to adapt to specific field formats and unit conversions to avoid deviations in extraction results. Multi-source, layered update data requires plugins to connect to different types of interfaces, and configure scheduled calling strategies based on update frequencies. The high proportion of long text requires tools to support segmented processing and context association to avoid information truncation. Core data sources deployed on internal networks limit the network access scope of plugins. Configure proxy endpoints accessible within the internal network to ensure data security.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
PARSE_STRUCTURED_FIELD | Enabled | Large state-owned bank due diligence reports contain a large number of standardized credit fields, requiring precise extraction of structured content |
MCP_SERVER_PROXY_ENDPOINT | Internal IP:Port | Core data of large state-owned banks is deployed on internal networks, to avoid public network exposure risks |
TOOL_CALL_TIMEOUT | 300 seconds | Single due diligence report has large data volume, and interface calls typically take longer |
CONTEXT_WINDOW_SIZE | 8000-12000 characters | Single complete due diligence report document typically has a large length, adapting to long text processing needs |
GLOBAL_VAR_SYNC_MODE | External request injection | Global variables such as institution code and query cycle need to be passed through external calls |
MCP_TOOL_VERSION | 4.96 | Matches the mainstream stable version feedback from the community, adapting to local MCP Server deployment |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Analyze specific issues individually, and conduct testing on your own samples before finalizing settings.
Three Common Misconfigurations
- Symptom: Global variables are empty when calling externally published interfaces, and preset parameters are not included in interface return results. Cause:
GLOBAL_VAR_SYNC_MODEis not configured to external request injection mode, resulting in global variables not being passed along the request chain. - Symptom: Text extraction tool returns a "field format mismatch" error, and extraction results do not match actual credit fields. Cause:
PARSE_STRUCTURED_FIELDis not enabled, or the extraction rule template adapted to large state-owned bank credit fields is not specified. - Symptom: Locally deployed MCP Server cannot be called, and logs show connection timeout errors. Cause:
MCP_SERVER_PROXY_ENDPOINTis filled withlocalhost:Portinstead of an internally accessible IP address, causing cross-node call failures. This issue is common in the 4.96 version of the MCP toolset.
How to Verify Successful Configuration
- Send an external call request carrying preset global variable parameters, and check whether the interface return result correctly includes the variable.
- Upload a single large state-owned bank due diligence report document, and check whether the tool extraction result includes preset credit fields with matching expected units.
- Start the local MCP Server, initiate a call through the FastGPT MCP tool test panel, and confirm that the return result is normal with no connection errors.
- Check the tool calling logs to confirm that the timeout setting matches the actual time required for document processing, with no frequent timeout alerts.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.